{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "97bf8340-2c4f-4b32-9a64-5b8ed2d6247f",
   "metadata": {},
   "source": [
    "# Fine-tuning a Vision Transformer Model With a Custom Biomedical Dataset\n",
    "_Authored by: [Emre Albayrak](https://github.com/emre570)_\n",
    "\n",
    "This guide outlines the process for fine-tuning a Vision Transformer (ViT) model on a custom biomedical dataset. It includes steps for loading and preparing the dataset, setting up image transformations for different data splits, configuring and initializing the ViT model, and defining the training process with evaluation and visualization tools.\n",
    "\n",
    "## Dataset Info\n",
    "The custom dataset is hand-made, containing 780 images with 3 classes (benign, malignant, normal). \n",
    "\n",
    "![attachment:datasetinfo.png](https://huggingface.co/datasets/huggingface/cookbook-images/resolve/102d6c23e6cc24db857fbc60186461ded6cdfb75/datasetinfo.png)\n",
    "\n",
    "## Model Info\n",
    "The model we fine-tune will be Google's [`\"vit-large-patch16-224\"`](https://huggingface.co/google/vit-large-patch16-224). It is trained on ImageNet-21k (14M images, 21.843 classes), and fine-tuned on ImageNet 2012 (1M images, 1.000 classes) at resolution 224x224. Google has several other ViT models with different image sizes and patches.\n",
    "\n",
    "Let's get started."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3cc02613-7bc6-4cd8-aa97-a21ba1970027",
   "metadata": {},
   "source": [
    "## Getting Started\n",
    "First, let's install libraries first."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7093dd4f-d0cb-44dc-935d-d54435187901",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install datasets transformers accelerate torch torchvision scikit-learn matplotlib wandb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b5019a8-d130-4c08-9503-cd8415f50ae9",
   "metadata": {},
   "source": [
    "(Optional) We will push our model to Hugging Face Hub so we must login."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3d5acb41-a225-44a5-8c8f-f212c615008f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4a75a73de5234297a7e0e4e070eee6d9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#from huggingface_hub import notebook_login\n",
    "#notebook_login()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80f2b730-ff8a-4d76-9baa-41c35b80fd38",
   "metadata": {},
   "source": [
    "## Dataset Preparation\n",
    "Datasets library automatically pulls images and classes from the dataset. For detailed info, you can visit [`this link`](https://huggingface.co/docs/datasets/image_load)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f6e69664-1bb0-4ee4-932c-2f108550bfba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 624\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 156\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "\n",
    "dataset = load_dataset(\"emre570/breastcancer-ultrasound-images\")\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf3176b2-65ff-44ea-bb1e-cd75b2d7609d",
   "metadata": {},
   "source": [
    "We got our dataset. But we don't have a validation set. To create the validation set, we will calculate the size of the validation set as a fraction of the training set based on the size of the test set. Then we split the training dataset into new training and validation subsets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4d385bab-dad0-462d-9339-11205fcd2e63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 468\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 156\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the numbers of each set\n",
    "test_num = len(dataset[\"test\"])\n",
    "train_num = len(dataset[\"train\"])\n",
    "\n",
    "val_size = test_num / train_num\n",
    "\n",
    "train_val_split = dataset[\"train\"].train_test_split(test_size=val_size)\n",
    "train_val_split"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dacfc60-6262-4e91-b9e9-e2af43210fde",
   "metadata": {},
   "source": [
    "We got our seperated train set. Let's merge them with test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b43bf130-49ad-4a4d-8230-436649045a16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 468\n",
       "    })\n",
       "    validation: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 156\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['image', 'label'],\n",
       "        num_rows: 156\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datasets import DatasetDict\n",
    "\n",
    "dataset = DatasetDict({\n",
    "    \"train\": train_val_split[\"train\"],\n",
    "    \"validation\": train_val_split[\"test\"],\n",
    "    \"test\": dataset[\"test\"]\n",
    "})\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a0d7a89-099e-4925-97a5-51c91cdb7046",
   "metadata": {},
   "source": [
    "Perfect! Our dataset is ready. Let's assign subsets to different variables. We will use them later for easy reference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9462e82b-0a12-4b0c-b73e-8dba76041ee6",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds = dataset['train']\n",
    "val_ds = dataset['validation']\n",
    "test_ds = dataset['test']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27e5451f-e2fb-4d29-b63a-a8960686dcc2",
   "metadata": {},
   "source": [
    "We can see the image is a PIL.Image with a label associated with it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1d8ffe26-f128-4f63-962e-56349d45ff23",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=460x391>,\n",
       " 'label': 0}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ds[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "384a09b0-1c47-411f-b91a-00acdd88b06b",
   "metadata": {},
   "source": [
    "We can also see the features of train set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1e09647b-44e9-4f5f-800c-c333b0523b85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'image': Image(mode=None, decode=True, id=None),\n",
       " 'label': ClassLabel(names=['benign', 'malignant', 'normal'], id=None)}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ds.features"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3fedab5-0e80-492c-9408-f629b230351d",
   "metadata": {},
   "source": [
    "Let's show one image from each class from dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5c901865-a876-4b4b-b1f2-8895b494cafb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Initialize a set to keep track of shown labels\n",
    "shown_labels = set()\n",
    "\n",
    "# Initialize the figure for plotting\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "# Loop through the dataset and plot the first image of each label\n",
    "for i, sample in enumerate(train_ds):\n",
    "    label = train_ds.features['label'].names[sample['label']]\n",
    "    if label not in shown_labels:\n",
    "        plt.subplot(1, len(train_ds.features['label'].names), len(shown_labels) + 1)\n",
    "        plt.imshow(sample['image'])\n",
    "        plt.title(label)\n",
    "        plt.axis('off')\n",
    "        shown_labels.add(label)\n",
    "        if len(shown_labels) == len(train_ds.features['label'].names):\n",
    "            break\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0300a4b7-4f8f-4155-bad8-42df6673eddc",
   "metadata": {},
   "source": [
    "## Data Processing\n",
    "The dataset is ready. But we are not ready for fine-tuning. We will follow this procedures respectively:\n",
    "\n",
    "- **Label Mapping:** We convert between label IDs and their corresponding names, useful for model training and evaluation.\n",
    "\n",
    "- **Image Processing:** Then, we utilize the ViTImageProcessor to standardize input image sizes and applies normalization specific to the pretrained model. Also, will define different transformations for training, validation, and testing to improve model generalization using torchvision.\n",
    "\n",
    "- **Transform Functions:** Implement functions to apply the transformations to the dataset, converting images to the required format and dimensions for the ViT model.\n",
    "\n",
    "- **Data Loading:** Set up a custom collate function to properly batch images and labels, and create a DataLoader for efficient loading and batching during model training.\n",
    "\n",
    "- **Batch Preparation:** Retrieve and display the shape of data in a sample batch to verify correct processing and readiness for model input."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1463c15d-5b73-47dc-8113-a910e3cd38b9",
   "metadata": {},
   "source": [
    "### Label Mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "43a6187f-0ba0-4cd9-a9ec-f06fca3a91bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({0: 'benign', 1: 'malignant', 2: 'normal'}, 'benign')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id2label = {id:label for id, label in enumerate(train_ds.features['label'].names)}\n",
    "label2id = {label:id for id,label in id2label.items()}\n",
    "id2label, id2label[train_ds[0]['label']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be0b208-5aad-48ff-97f5-ea484cfc8ad7",
   "metadata": {},
   "source": [
    "### Image Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a2d04160-93aa-425e-a06d-58c09ec6ffbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import ViTImageProcessor\n",
    "\n",
    "model_name = \"google/vit-large-patch16-224\"\n",
    "processor = ViTImageProcessor.from_pretrained(model_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "136d114f-a054-467e-a034-cdddf9bf574b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torchvision.transforms import CenterCrop, Compose, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor, Resize\n",
    "\n",
    "image_mean, image_std = processor.image_mean, processor.image_std\n",
    "size = processor.size[\"height\"]\n",
    "\n",
    "normalize = Normalize(mean=image_mean, std=image_std)\n",
    "\n",
    "train_transforms = Compose([        \n",
    "    RandomResizedCrop(size),\n",
    "    RandomHorizontalFlip(),\n",
    "    ToTensor(),\n",
    "    normalize,\n",
    "])\n",
    "val_transforms = Compose([\n",
    "    Resize(size),\n",
    "    CenterCrop(size),\n",
    "    ToTensor(),\n",
    "    normalize,\n",
    "])\n",
    "test_transforms = Compose([\n",
    "    Resize(size),\n",
    "    CenterCrop(size),\n",
    "    ToTensor(),\n",
    "    normalize,\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e910499-84bf-4672-bbf0-ca78915b2821",
   "metadata": {},
   "source": [
    "### Create transform functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5ddc7ad4-bd09-4c76-ac00-ca7dafbd8417",
   "metadata": {},
   "outputs": [],
   "source": [
    "def apply_train_transforms(examples):\n",
    "    examples['pixel_values'] = [train_transforms(image.convert(\"RGB\")) for image in examples['image']]\n",
    "    return examples\n",
    "\n",
    "def apply_val_transforms(examples):\n",
    "    examples['pixel_values'] = [val_transforms(image.convert(\"RGB\")) for image in examples['image']]\n",
    "    return examples\n",
    "\n",
    "def apply_test_transforms(examples):\n",
    "    examples['pixel_values'] = [val_transforms(image.convert(\"RGB\")) for image in examples['image']]\n",
    "    return examples"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1ac74835-f883-46a3-877c-26265b27a325",
   "metadata": {},
   "source": [
    "### Apply transform functions to each set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "45f2b765-5258-4b44-b2ce-6bff952bdd1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_ds.set_transform(apply_train_transforms)\n",
    "val_ds.set_transform(apply_val_transforms)\n",
    "test_ds.set_transform(apply_test_transforms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ce586ef6-5f48-4554-8dc4-48797a977674",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'image': Image(mode=None, decode=True, id=None),\n",
       " 'label': ClassLabel(names=['benign', 'malignant', 'normal'], id=None)}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ds.features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "731fccb7-7311-4175-8ce1-67141a662b1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=460x391>,\n",
       " 'label': 0,\n",
       " 'pixel_values': tensor([[[-0.2000, -0.1765, -0.1529,  ..., -0.3098, -0.3490, -0.3412],\n",
       "          [-0.2471, -0.2392, -0.2471,  ..., -0.2392, -0.2235, -0.2000],\n",
       "          [-0.3255, -0.3412, -0.3647,  ..., -0.1765, -0.1608, -0.1529],\n",
       "          ...,\n",
       "          [-0.7333, -0.7412, -0.7647,  ..., -0.7490, -0.7647, -0.7725],\n",
       "          [-0.7255, -0.7176, -0.7333,  ..., -0.7882, -0.7804, -0.7882],\n",
       "          [-0.7412, -0.7333, -0.7412,  ..., -0.7804, -0.7725, -0.7804]],\n",
       " \n",
       "         [[-0.2000, -0.1765, -0.1529,  ..., -0.3098, -0.3490, -0.3412],\n",
       "          [-0.2471, -0.2392, -0.2471,  ..., -0.2392, -0.2235, -0.2000],\n",
       "          [-0.3255, -0.3412, -0.3647,  ..., -0.1765, -0.1608, -0.1529],\n",
       "          ...,\n",
       "          [-0.7333, -0.7412, -0.7647,  ..., -0.7490, -0.7647, -0.7725],\n",
       "          [-0.7255, -0.7176, -0.7333,  ..., -0.7882, -0.7804, -0.7882],\n",
       "          [-0.7412, -0.7333, -0.7412,  ..., -0.7804, -0.7725, -0.7804]],\n",
       " \n",
       "         [[-0.2000, -0.1765, -0.1529,  ..., -0.3098, -0.3490, -0.3412],\n",
       "          [-0.2471, -0.2392, -0.2471,  ..., -0.2392, -0.2235, -0.2000],\n",
       "          [-0.3255, -0.3412, -0.3647,  ..., -0.1765, -0.1608, -0.1529],\n",
       "          ...,\n",
       "          [-0.7333, -0.7412, -0.7647,  ..., -0.7490, -0.7647, -0.7725],\n",
       "          [-0.7255, -0.7176, -0.7333,  ..., -0.7882, -0.7804, -0.7882],\n",
       "          [-0.7412, -0.7333, -0.7412,  ..., -0.7804, -0.7725, -0.7804]]])}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_ds[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49b06fa4-6f28-4e45-8af4-779c424583fe",
   "metadata": {},
   "source": [
    "Looks like we converted our pixel values into tensors. \n",
    "\n",
    "### Data Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3f47f263-2046-4847-952d-728fa3fe5cf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.utils.data import DataLoader\n",
    "\n",
    "def collate_fn(examples):\n",
    "    pixel_values = torch.stack([example[\"pixel_values\"] for example in examples])\n",
    "    labels = torch.tensor([example[\"label\"] for example in examples])\n",
    "    return {\"pixel_values\": pixel_values, \"labels\": labels}\n",
    "\n",
    "train_dl = DataLoader(train_ds, collate_fn=collate_fn, batch_size=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aed986b0-e661-4c57-ad58-4fa73d795828",
   "metadata": {},
   "source": [
    "### Batch Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "14c6d3f8-48e6-423f-8193-7571f986f103",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pixel_values torch.Size([4, 3, 224, 224])\n",
      "labels torch.Size([4])\n"
     ]
    }
   ],
   "source": [
    "batch = next(iter(train_dl))\n",
    "for k,v in batch.items():\n",
    "  if isinstance(v, torch.Tensor):\n",
    "    print(k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35379d35-e567-4a30-8b91-6eac80b79044",
   "metadata": {},
   "source": [
    "Perfect! Now we are ready for fine-tuning process."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45056d5e-8bca-4ece-b29b-bc772aeef49f",
   "metadata": {},
   "source": [
    "## Fine-tuning the Model\n",
    "Now we will configure and fine-tune the model. We started by initializing the model with specific label mappings and pre-trained settings, adjusting for size mismatches. Training parameters are set up to define the model's learning process, including the save strategy, batch sizes, and training epochs, with results logged via Weights & Biases. Hugging Face Trainer will then instantiate to manage the training and evaluation, utilizing a custom data collator and the model's built-in processor. Finally, after training, the model's performance is evaluated on a test dataset, with metrics printed to assess its accuracy.\n",
    "\n",
    "First, we call our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "97d0b588-3e41-4852-9307-9e2ec7d5bb0b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of ViTForImageClassification were not initialized from the model checkpoint at google/vit-large-patch16-224 and are newly initialized because the shapes did not match:\n",
      "- classifier.weight: found shape torch.Size([1000, 1024]) in the checkpoint and torch.Size([3, 1024]) in the model instantiated\n",
      "- classifier.bias: found shape torch.Size([1000]) in the checkpoint and torch.Size([3]) in the model instantiated\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "from transformers import ViTForImageClassification\n",
    "\n",
    "model = ViTForImageClassification.from_pretrained(model_name, id2label=id2label, label2id=label2id, ignore_mismatched_sizes=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f773e18-63e8-41bd-885f-7ba95d074a3d",
   "metadata": {},
   "source": [
    "There is a subtle detail in here. The `ignore_mismatched_sizes` parameter.\n",
    "\n",
    "When you fine-tune a pre-trained model on a new dataset, sometimes the input size of your images or the model architecture specifics (like the number of labels in the classification layer) might not match exactly with what the model was originally trained on. This can happen for various reasons, such as when using a model trained on one type of image data (like natural images from ImageNet) on a completely different type of image data (like medical images or specialized camera images).\n",
    "\n",
    "Setting `ignore_mismatched_sizes` to `True` allows the model to adjust its layers to accommodate size differences without throwing an error. \n",
    "\n",
    "For example, the number of classes this model is trained on is 1000, which is `torch.Size([1000])` and it expects an input with `torch.Size([1000])` classes. Our dataset has 3, which is `torch.Size([3])` classes. If we give it directly, it will raise an error because the class numbers do not match."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2c671bf-9978-46d8-82ef-0906d4e89d03",
   "metadata": {},
   "source": [
    "Then, define training arguments from Google for this model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d473af8a-4070-48d7-aecb-1a5a90f0b63f",
   "metadata": {},
   "source": [
    "(Optional) Note that the metrics will be saved in Weights & Biases because we set the `report_to` parameter to `wandb`. W&B will ask you for an API key, so you should create an account and an API key. If you don't want, you can remove `report_to` parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f16fc568-9fdc-4c60-acec-6ed3dbb85aef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import TrainingArguments, Trainer\n",
    "import numpy as np\n",
    "\n",
    "train_args = TrainingArguments(\n",
    "    output_dir = \"output-models\",\n",
    "    save_total_limit=2,\n",
    "    report_to=\"wandb\",\n",
    "    save_strategy=\"epoch\",\n",
    "    evaluation_strategy=\"epoch\",\n",
    "    learning_rate=2e-5,\n",
    "    per_device_train_batch_size=10,\n",
    "    per_device_eval_batch_size=4,\n",
    "    num_train_epochs=40,\n",
    "    weight_decay=0.01,\n",
    "    load_best_model_at_end=True,\n",
    "    logging_dir='logs',\n",
    "    remove_unused_columns=False,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f74d4457-49fd-4e1b-9842-a3759ec524c9",
   "metadata": {},
   "source": [
    "We can now begin the fine-tuning process with `Trainer`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5a117d62-9054-4e14-b7b2-0703de17a741",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "      \n",
       "      <progress value='1880' max='1880' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      [1880/1880 16:42, Epoch 40/40]\n",
       "    </div>\n",
       "    <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       " <tr style=\"text-align: left;\">\n",
       "      <th>Epoch</th>\n",
       "      <th>Training Loss</th>\n",
       "      <th>Validation Loss</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.428426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.394955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.370801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.364052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.427605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.441180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.377579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.387463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.380499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>No log</td>\n",
       "      <td>0.346761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.390300</td>\n",
       "      <td>0.469292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.390300</td>\n",
       "      <td>0.389932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.390300</td>\n",
       "      <td>0.435536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.390300</td>\n",
       "      <td>0.296190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.390300</td>\n",
       "      <td>0.436435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.390300</td>\n",
       "      <td>0.446079</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.390300</td>\n",
       "      <td>0.577235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.390300</td>\n",
       "      <td>0.401280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.390300</td>\n",
       "      <td>0.501154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>0.390300</td>\n",
       "      <td>0.490980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>0.390300</td>\n",
       "      <td>0.458035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>22</td>\n",
       "      <td>0.238200</td>\n",
       "      <td>0.426354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>23</td>\n",
       "      <td>0.238200</td>\n",
       "      <td>0.411909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>0.238200</td>\n",
       "      <td>0.435578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>0.238200</td>\n",
       "      <td>0.430924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>26</td>\n",
       "      <td>0.238200</td>\n",
       "      <td>0.498050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>27</td>\n",
       "      <td>0.238200</td>\n",
       "      <td>0.501461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>28</td>\n",
       "      <td>0.238200</td>\n",
       "      <td>0.559837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>29</td>\n",
       "      <td>0.238200</td>\n",
       "      <td>0.420119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>30</td>\n",
       "      <td>0.238200</td>\n",
       "      <td>0.416809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>31</td>\n",
       "      <td>0.238200</td>\n",
       "      <td>0.635555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>32</td>\n",
       "      <td>0.163100</td>\n",
       "      <td>0.421264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>0.163100</td>\n",
       "      <td>0.445050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>0.163100</td>\n",
       "      <td>0.453854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>0.163100</td>\n",
       "      <td>0.442983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>36</td>\n",
       "      <td>0.163100</td>\n",
       "      <td>0.432370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>37</td>\n",
       "      <td>0.163100</td>\n",
       "      <td>0.442086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>38</td>\n",
       "      <td>0.163100</td>\n",
       "      <td>0.478380</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>39</td>\n",
       "      <td>0.163100</td>\n",
       "      <td>0.477927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>0.163100</td>\n",
       "      <td>0.479882</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table><p>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "TrainOutput(global_step=1880, training_loss=0.23721330723863968, metrics={'train_runtime': 1003.2398, 'train_samples_per_second': 18.66, 'train_steps_per_second': 1.874, 'total_flos': 5.128065177052447e+18, 'train_loss': 0.23721330723863968, 'epoch': 40.0})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainer = Trainer(\n",
    "    model,\n",
    "    train_args,\n",
    "    train_dataset=train_ds,\n",
    "    eval_dataset=val_ds,\n",
    "    data_collator=collate_fn,\n",
    "    tokenizer=processor,\n",
    ")\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e154fd79-1de7-4169-a6af-402b12881042",
   "metadata": {},
   "source": [
    "| Epoch | Training Loss | Validation Loss | Accuracy |\n",
    "|-------|---------------|-----------------|----------|\n",
    "| 40    | 0.174700      | 0.596288        | 0.903846 |\n",
    "\n",
    "The fine-tuning process is done. Let's continue with evaluating the model to test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c19b6d99-0a89-45ac-a6d9-ec3e79edc041",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'test_loss': 0.40843912959098816, 'test_runtime': 4.9934, 'test_samples_per_second': 31.242, 'test_steps_per_second': 7.81}\n"
     ]
    }
   ],
   "source": [
    "outputs = trainer.predict(test_ds)\n",
    "print(outputs.metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c4ddecb-7ab0-493e-90b9-44bf4e2a530e",
   "metadata": {},
   "source": [
    "`{'test_loss': 0.3219967782497406, 'test_accuracy': 0.9102564102564102, 'test_runtime': 4.0543, 'test_samples_per_second': 38.478, 'test_steps_per_second': 9.619}`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0be50a0b",
   "metadata": {},
   "source": [
    "### (Optional) Push Model to Hub\n",
    "We can push our model to Hugging Face Hub using `push_to_hub`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1d55e6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.push_to_hub(\"your_model_name\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f74c058-e2a5-4c9c-8d70-1c2c574b933f",
   "metadata": {},
   "source": [
    "That's great! Let's visualize the results.\n",
    "\n",
    "## Results\n",
    "We made the fine-tuning. Let's see how our model predicted the classes using scikit-learn's Confusion Matrix Display and show Recall Score."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ade5321d-ff63-4308-8317-d1e4da2219df",
   "metadata": {},
   "source": [
    "### What is Confusion Matrix?\n",
    "A confusion matrix is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning model, on a set of test data for which the true values are known. It's especially useful for checking how well a classification model is performing because it shows the frequency of true versus predicted labels.\n",
    "\n",
    "Let's draw our model's Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "8efb0ece-92b3-498d-b47b-0f9c04d4ebb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x7f44b1f289a0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RkUxx2IiIiIhMij6eCs2nShMRERHpiJUXIiIimVLBDCod6xS6Hl8YmLwQERHJlFyHjZi8EBERyZQaZlDrWDnR9fjCYHwREREREb0CKy9EREQypRIFqHQc9tH1+MLA5IWIiEim5NrzwmEjIiIi0pt79+7hs88+g5ubG2xtbVGlShWcOXNG2i6KIiZMmABPT0/Y2toiMDAQ169f1+oaTF6IiIhkShTNoNZxEbW4w+7jx4/x3nvvwdLSEr/++iv+/PNPzJ07Fy4uLtI+s2bNwsKFC7Fs2TKcOnUK9vb2CAoKQnZ2doGvw2EjIiIimVJBgErHBytqc/xXX30Fb29vREVFSet8fX2lP4uiiPnz52PcuHFo27YtAGDdunVwd3fHjh078MknnxToOqy8EBER0Wulp6drLEql8oV9du7cidq1a6Nz584oUaIEatSogRUrVkjbb926heTkZAQGBkrrFAoF6tWrhxMnThQ4FiYvREREMqUW/2naffMl/1ze3t5QKBTSEhER8cL1bt68iaVLl6J8+fL47bffMGDAAAwdOhRr164FACQnJwMA3N3dNY5zd3eXthUEh42IiIhk6lnfiq7nAICEhAQ4OTlJ662trV/cV61G7dq1MWPGDABAjRo1cOnSJSxbtgw9evTQKY7nsfJCREREr+Xk5KSxvCx58fT0RKVKlTTWVaxYEfHx8QAADw8PAEBKSorGPikpKdK2gmDyQkREJFNqCHpZCuq9995DbGysxrpr166hdOnSAPKbdz08PLBv3z5pe3p6Ok6dOoWAgIACX4fDRkRERDJV1HfYDQ0NRYMGDTBjxgx06dIFv//+O7755ht88803AABBEDB8+HBMmzYN5cuXh6+vL8aPHw8vLy+0a9euwNdh8kJERCRT+ux5KYg6depg+/btGDt2LKZMmQJfX1/Mnz8f3bp1k/YZNWoUsrKy0K9fP6SmpuL999/H7t27YWNjU+DrMHkhIiIivWndujVat279n9sFQcCUKVMwZcqUN74GkxciIiKZUkMPzzbS8SZ3hYHJCxERkUyJWjbc/tc5jA1nGxEREZFJYeWFiIhIpp7dJVfXcxgbJi9EREQyVdSzjYqK8UVERERE9AqsvBAREckUh42IiIjIpGh7e///Ooex4bARERERmRRWXoiIiGSKw0ZERERkUpi8EBERkUmRa/LCnhciIiIyKay8EBERyZRcKy9MXoiIiGRKhO5TnUX9hKJXHDYiIiIik8LKCxERkUxx2IiIiIhMilyTFw4bERERkUlh5YWIiEim5Fp5YfJCREQkU3JNXjhsRERERCaFlRciIiKZEkUBoo6VE12PLwxMXoiIiGRKDUHnm9TpenxhYPJCREQkU+x5ISIiIjICrLwQERHJFHteiIiIyKRw2IiIiIjICLDyQkREJFMcNiKj0+n9D2BhZmXoMKiQxQ0rbugQqAiVmXDb0CFQIRNEEcgrmmuJehg2MsbkhcNGREREZFJYeSEiIpIpEYAo6n4OY8PkhYiISKbUECDI8A67HDYiIiIik8LKCxERkUzJdbYRKy9EREQy9ewmdbouBTVp0iQIgqCx+Pv7S9uzs7MxaNAguLm5wcHBAR07dkRKSorW74vJCxERkUyJon4WbVSuXBlJSUnScvToUWlbaGgodu3ahS1btuDQoUNITExEhw4dtH5fHDYiIiIivbGwsICHh8cL69PS0rBq1Sps2LABH3zwAQAgKioKFStWxMmTJ1G/fv0CX4OVFyIiIpl61vOi6wIA6enpGotSqXzpNa9fvw4vLy+UKVMG3bp1Q3x8PADg7NmzyM3NRWBgoLSvv78/SpUqhRMnTmj1vpi8EBERyZQ+kxdvb28oFAppiYiIeOF69erVw5o1a7B7924sXboUt27dQsOGDZGRkYHk5GRYWVnB2dlZ4xh3d3ckJydr9b44bERERESvlZCQACcnJ+m1tbX1C/t89NFH0p+rVq2KevXqoXTp0ti8eTNsbW31FgsrL0RERDKlz9lGTk5OGsvLkpd/c3Z2RoUKFRAXFwcPDw/k5OQgNTVVY5+UlJSX9si8CpMXIiIimTLEbKPnZWZm4saNG/D09EStWrVgaWmJffv2SdtjY2MRHx+PgIAArc7LYSMiIiLSi/DwcAQHB6N06dJITEzExIkTYW5ujq5du0KhUCAkJARhYWFwdXWFk5MThgwZgoCAAK1mGgFMXoiIiGQrv3Ki6x12C77v3bt30bVrVzx8+BDFixfH+++/j5MnT6J48eIAgMjISJiZmaFjx45QKpUICgrCkiVLtI6JyQsREZFMFfXjATZu3PjK7TY2Nli8eDEWL16sU0zseSEiIiKTwsoLERGRTIl/L7qew9gweSEiIpIpuT5VmskLERGRXMm09MKeFyIiIjIprLwQERHJlR6GjcBhIyIiIioqut4h99k5jA2HjYiIiMiksPJCREQkU5xtRERERKZFFHTvWTHC5IXDRkRERGRSWHkhIiKSKbk27DJ5ISIikiuZ3qSOyQsREZFMvdUNuzt37izwCdu0afPGwRARERG9ToGSl3bt2hXoZIIgQKVS6RIPERER6ZMRDvvoqkDJi1qtLuw4iIiISM/kOmyk01Tp7OxsfcVBREREVCBaJy8qlQpTp07FO++8AwcHB9y8eRMAMH78eKxatUrvARIREdEbEvW0GBmtk5fp06djzZo1mDVrFqysrKT17777LlauXKnX4IiIiEgXgp4W46J18rJu3Tp888036NatG8zNzaX11apVw9WrV/UaHBEREdG/aX2fl3v37qFcuXIvrFer1cjNzdVLUERERKQHMr1JndaVl0qVKuHIkSMvrN+6dStq1Kihl6CIiIhID2Ta86J15WXChAno0aMH7t27B7VajW3btiE2Nhbr1q3DTz/9VBgxEhEREUm0rry0bdsWu3btwt69e2Fvb48JEybgypUr2LVrF5o3b14YMRIREdGbEAX9LEbmjZ5t1LBhQ0RHR+s7FiIiItIjPlX6X86cOYMrV64AyO+DqVWrlt6CIiIiIj2QacOu1snL3bt30bVrVxw7dgzOzs4AgNTUVDRo0AAbN25EyZIl9R0jERERkUTrnpc+ffogNzcXV65cwaNHj/Do0SNcuXIFarUaffr0KYwYiYiI6E2w5yXfoUOHcPz4cfj5+Unr/Pz8sGjRIjRs2FCvwREREdGbE8T8RddzGButKy/e3t4vvRmdSqWCl5eXXoIiIiIi+i9aJy+zZ8/GkCFDcObMGWndmTNnMGzYMMyZM0evwREREZEO3uab1Lm4uEAQ/hnzysrKQr169WBhkX94Xl4eLCws0Lt3b7Rr165QAiUiIiIt6aNnxVR7XubPn1/IYRAREREVTIGSlx49ehR2HERERKRvvM/Li7Kzs5GTk6OxzsnJSaeAiIiISE9kmrxo3bCblZWFwYMHo0SJErC3t4eLi4vGQkRERFSYtE5eRo0ahf3792Pp0qWwtrbGypUrMXnyZHh5eWHdunWFESMRERG9CQPPNpo5cyYEQcDw4cOlddnZ2Rg0aBDc3Nzg4OCAjh07IiUlRavzap287Nq1C0uWLEHHjh1hYWGBhg0bYty4cZgxYwa+++47bU9HREREhcWAd9g9ffo0li9fjqpVq2qsDw0Nxa5du7BlyxYcOnQIiYmJ6NChg1bn1jp5efToEcqUKQMgv7/l0aNHAID3338fhw8f1vZ0REREVEie3WFX10VbmZmZ6NatG1asWKHRUpKWloZVq1Zh3rx5+OCDD1CrVi1ERUXh+PHjOHnyZIHPr3XDbpkyZXDr1i2UKlUK/v7+2Lx5M+rWrYtdu3ZJD2o0pJ49eyI1NRU7duwAADRp0gTVq1fndG8T1e2LG+jW/6bGuoRbdviiw3sGioj0pavfZXT1u4ySDhkAgOuprlh8oRYO3ysl7VO9eDJCa/6OasXuQy0KuPKoGHpHt4JSpdNcAzIC79bNQKf+KShf5Qnc3HMxuU9ZnNjjbOiw6BXS09M1XltbW8Pa2vql+w4aNAitWrVCYGAgpk2bJq0/e/YscnNzERgYKK3z9/dHqVKlcOLECdSvX79AsWj9E6BXr164cOECGjdujDFjxiA4OBhff/01cnNzMW/ePG1PV+i2bdsGS0tLQ4fxUj4+Phg+fLjGWCC96HacPb7sX0t6rVIZ3w2TSHvJWfaYe7YebqcrIAhA+7KxWPLBbrTb1Qlxqa6oXjwZq5r/guUXa2DqqfehUpvB3/UvqI3whlmkPRs7NW79aYs9m9wwYcXN1x9Ab0aPs428vb01Vk+cOBGTJk16YfeNGzfi3LlzOH369AvbkpOTYWVl9UKxw93dHcnJyQUOSevkJTQ0VPpzYGAgrl69irNnz6JcuXIvjGsZA1dXV0OHQDpSqQQ8fvjy7J5M14G7PhqvI2Pqoav/n6hePAVxqa74X93jWHflXXxzsYa0z61056INkgrNmYMKnDmoMHQYpIWEhASN26G8rOqSkJCAYcOGITo6GjY2NoUWi9Y9L/9WunRpdOjQ4Y0SlyZNmmDIkCEYPnw4XFxc4O7ujhUrViArKwu9evWCo6MjypUrh19//RVA/sMfQ0JC4OvrC1tbW/j5+WHBggWvvcbzlY2kpCS0atUKtra28PX1xYYNG+Dj46MxrCQIAlauXIn27dvDzs4O5cuXx86dO6XtBYmjZ8+eaNeuHebMmQNPT0+4ublh0KBB0kMtmzRpgjt37iA0NBSCIGg8foE0vVPqCdbvOYRVu45i5PSLKO7x1NAhkZ6ZCWq08o2DnUUuYu67w9XmKaoXv49HT22xseV2HP94Lb5t8SNqlUgydKhEby0nJyeN5WXJy9mzZ3H//n3UrFkTFhYWsLCwwKFDh7Bw4UJYWFjA3d0dOTk5SE1N1TguJSUFHh4eBY6lQJWXhQsXFviEQ4cOLfC+ALB27VqMGjUKv//+OzZt2oQBAwZg+/btaN++Pf73v/8hMjISn3/+OeLj42FpaYmSJUtiy5YtcHNzw/Hjx9GvXz94enqiS5cuBbpe9+7d8ddff+HgwYOwtLREWFgY7t+//8J+kydPxqxZszB79mwsWrQI3bp1w507d+Dq6gq1Wl2gOA4cOABPT08cOHAAcXFx+Pjjj1G9enX07dsX27ZtQ7Vq1dCvXz/07dv3lTErlUoolUrp9b/HHeUs9pIC8ya8i7t37OBaTIlPv7iJ2avPYECnADx9wr4HU1fB+SE2tdoOa3MVnuRZYtD+INxIc0W14vnTJgdXP4OvzgTgyqNiaFc2FmuDdqHVji64k+Fs2MCJTISAN2u4/fc5CqpZs2a4ePGixrpevXrB398fo0ePhre3NywtLbFv3z507NgRABAbG4v4+HgEBAQU+DoF+ukfGRlZoJMJgqB18lKtWjWMGzcOADB27FjMnDkTxYoVk77QJ0yYgKVLl+KPP/5A/fr1MXnyZOlYX19fnDhxAps3by5Q8nL16lXs3bsXp0+fRu3atQEAK1euRPny5V/Yt2fPnujatSsAYMaMGVi4cCF+//13tGjRApaWlgWKw8XFBV9//TXMzc3h7++PVq1aYd++fejbty9cXV1hbm4OR0fH12abERERGtd7m5w5Vkz68+3rjoi9qMCaX46i4Ycp2LPjHQNGRvpwK90ZbXd2hqNVDlqUvomvGh5At1/bwOzvQfZN1yphW5w/AODKo2II8LyHTuVjMfdcPUOGTWQ6ivjBjI6Ojnj33Xc11tnb28PNzU1aHxISgrCwMLi6usLJyQlDhgxBQEBAgZt1gQImL7du3SrwCbX1/HCTubk53NzcUKVKFWmdu7s7AEjVkcWLF2P16tWIj4/H06dPkZOTg+rVqxfoWrGxsbCwsEDNmjWldeXKlXvpnYGfj8ve3h5OTk4aFZqCxFG5cmWYm5tLrz09PV/ISAti7NixCAsLk16np6e/0Dj1tsjKtMS9eDt4eT8xdCikB7lqc8Rn5Pc9XH5YHFWK3UePShelPpe4VM3/mzfTXOBpn1HkcRKR/kRGRsLMzAwdO3aEUqlEUFAQlixZotU5DF53//dMIEEQNNY96wVRq9XYuHEjwsPDMXfuXAQEBMDR0RGzZ8/GqVOniiQutVoNAAWO41Xn0MarpqO9bWxs8+BZ8gn2/+xp6FCoEAiCCCtzFe5mOiIlyw6+ilSN7T5OqRpTqYnoNYzg2UYHDx7UeG1jY4PFixdj8eLFb3xOgycv2jh27BgaNGiAgQMHSutu3LhR4OP9/PyQl5eHmJgY1KqVP/U2Li4Ojx8/LtI4nrGysoJKpdL6uLdJSOg1nDpcDPcTbeFWQonP+t+AWi3g4O6CN3aRcRpR8xQO3fNGUpYD7C1yEVwmDvU8EtF7TysAAlZero6h1c/g6iM3XHlUDO3LxaKMIhVDDn5o6NBJD2zsVPDy+aeXz8NbiTKVniAj1QIPEq0MGJnMGEHyUhhMKnkpX7481q1bh99++w2+vr5Yv349Tp8+DV9f3wId7+/vj8DAQPTr1w9Lly6FpaUlRowYAVtbW61m++gaxzM+Pj44fPgwPvnkE1hbW6NYsWKvP+gtU8w9G6MjLsJJkYu0x1a4fN4Zod3rIv0xf7iZOlebp5jVcD9K2D5BRo4VYh+7ofeeVjielD8kuvbPqrA2V+F/dY9DYaXE1cdu6LWnNRIyOL1WDipUfYJZm69Jr7+YeBcAEL3FDXNH+BgoKjIVJpW8fPHFF4iJicHHH38MQRDQtWtXDBw4UJpKXRDr1q1DSEgIGjVqBA8PD0RERODy5ctazUfXRxwAMGXKFHzxxRcoW7YslEolRNEI01sD+2qM8d07iPTjy+NNXrvPNxdraNznheTjj5OOaFGq1ut3JJ286e39/30OYyOIb/k35t27d+Ht7Y29e/eiWbNmhg6nQNLT06FQKNCsRB9YmLECIXdxw8oaOgQqQmUmvHhXUpKXPDEXB/J+QFpamsZN3/Tp2feEz7TpMNPxZnHq7GzcHvdlocarrTe6Sd2RI0fw2WefISAgAPfu3QMArF+/HkePHtVrcIVh//792LlzJ27duoXjx4/jk08+gY+PDxo1amTo0IiIiPRL1NNiZLROXn744QcEBQXB1tYWMTEx0s3T0tLSMGPGDL0HqG+5ubn43//+h8qVK6N9+/YoXry4dMM6IiIiMn5aJy/Tpk3DsmXLsGLFCo0v/Pfeew/nzp3Ta3CFISgoCJcuXcKTJ0+QkpKC7du3o3Tp0oYOi4iISO+e9bzouhgbrRt2Y2NjXzrEolAoXnhWARERERlQEd9ht6hoXXnx8PBAXFzcC+uPHj2KMmXK6CUoIiIiov+idfLSt29fDBs2DKdOnYIgCEhMTMR3332H8PBwDBgwoDBiJCIiojch04ZdrYeNxowZA7VajWbNmuHJkydo1KgRrK2tER4ejiFDhhRGjERERPQG5HqfF62TF0EQ8OWXX2LkyJGIi4tDZmYmKlWqBAcHh8KIj4iIiEjDG99h18rKCpUqVdJnLERERKRPfLZRvqZNm77yOUD79+/XKSAiIiLSE31MdZZD8lK9enWN17m5uTh//jwuXbqEHj166CsuIiIiopfSOnmJjIx86fpJkyYhMzNT54CIiIhIT2Q6bPRGzzZ6mc8++wyrV6/W1+mIiIhIV5wq/WonTpyAjY5PriQiIiL94VTpv3Xo0EHjtSiKSEpKwpkzZzB+/Hi9BUZERET0MlonLwqFQuO1mZkZ/Pz8MGXKFHz44Yd6C4yIiIjoZbRKXlQqFXr16oUqVarAxcWlsGIiIiIifWDDLmBubo4PP/yQT48mIiIig9F6ttG7776LmzdvFkYsREREpEfPGnZ1XYyN1snLtGnTEB4ejp9++glJSUlIT0/XWIiIiMiIyGyaNKBFz8uUKVMwYsQItGzZEgDQpk0bjccEiKIIQRCgUqn0HyURERHR3wqcvEyePBn9+/fHgQMHCjMeIiIi0heZNuwWOHkRxfzoGzduXGjBEBERkf7I9SZ1WvW8vOpp0kRERERFQav7vFSoUOG1CcyjR490CoiIiIj05G0fNgLy+17+fYddIiIiMk5yHTbSKnn55JNPUKJEicKKhYiIiPRJppWXAve8sN+FiIiIjIHWs42IiIjIRMi08lLg5EWtVhdmHERERKRncu150frxAERERESGpFXDLhEREZkQmQ4bsfJCREQkV7o+lFHL5Gfp0qWoWrUqnJyc4OTkhICAAPz666/S9uzsbAwaNAhubm5wcHBAx44dkZKSovXbYvJCREREelGyZEnMnDkTZ8+exZkzZ/DBBx+gbdu2uHz5MgAgNDQUu3btwpYtW3Do0CEkJiaiQ4cOWl+Hw0ZEREQyVdQNu8HBwRqvp0+fjqVLl+LkyZMoWbIkVq1ahQ0bNuCDDz4AAERFRaFixYo4efIk6tevX+DrsPJCREQkV3ocNkpPT9dYlErlKy+tUqmwceNGZGVlISAgAGfPnkVubi4CAwOlffz9/VGqVCmcOHFCq7fF5IWIiIhey9vbGwqFQloiIiJeut/Fixfh4OAAa2tr9O/fH9u3b0elSpWQnJwMKysrODs7a+zv7u6O5ORkrWLhsBEREZFM6XPYKCEhAU5OTtJ6a2vrl+7v5+eH8+fPIy0tDVu3bkWPHj1w6NAh3YL4FyYvREREcqXHqdLPZhC9jpWVFcqVKwcAqFWrFk6fPo0FCxbg448/Rk5ODlJTUzWqLykpKfDw8NAqJA4bERERyVURT5V+GbVaDaVSiVq1asHS0hL79u2TtsXGxiI+Ph4BAQFanZOVFyIiItKLsWPH4qOPPkKpUqWQkZGBDRs24ODBg/jtt9+gUCgQEhKCsLAwuLq6wsnJCUOGDEFAQIBWM40AJi9ERESyJfy96HqOgrp//z66d++OpKQkKBQKVK1aFb/99huaN28OAIiMjISZmRk6duwIpVKJoKAgLFmyROuYmLwQERHJVRE/HmDVqlWv3G5jY4PFixdj8eLFOoXEnhciIiIyKay8EBERyVRR32G3qDB5ISIikis+VZqIiIjI8Fh5ISIikjMjrJzoiskLERGRTMm154XDRkRERGRSWHkhIiKSK5k27DJ5ISIikim5DhsxeSEiIpIrmVZe2PNCREREJoWVFxMmPn0KUVAZOgwqZGUjLhk6BCpC6e1rGToEKmR5udnA9h+K5FocNiIiIiLTwmEjIiIiIsNj5YWIiEiuZFp5YfJCREQkU3LteeGwEREREZkUVl6IiIjkisNGREREZEoEUYQg6pZ96Hp8YeCwEREREZkUVl6IiIjkisNGREREZErkOtuIyQsREZFcybTywp4XIiIiMimsvBAREckUh42IiIjItHDYiIiIiMjwWHkhIiKSKQ4bERERkWnhsBERERGR4bHyQkREJGPGOOyjKyYvREREciWK+Yuu5zAyHDYiIiIik8LKCxERkUxxthERERGZFpnONmLyQkREJFOCOn/R9RzGhj0vREREpBcRERGoU6cOHB0dUaJECbRr1w6xsbEa+2RnZ2PQoEFwc3ODg4MDOnbsiJSUFK2uw+SFiIhIrkQ9LQV06NAhDBo0CCdPnkR0dDRyc3Px4YcfIisrS9onNDQUu3btwpYtW3Do0CEkJiaiQ4cOWr0tDhsRERHJVFE37O7evVvj9Zo1a1CiRAmcPXsWjRo1QlpaGlatWoUNGzbggw8+AABERUWhYsWKOHnyJOrXr1+g67DyQkRERK+Vnp6usSiVytcek5aWBgBwdXUFAJw9exa5ubkIDAyU9vH390epUqVw4sSJAsfC5IWIiEiunt2kTtcFgLe3NxQKhbRERES88tJqtRrDhw/He++9h3fffRcAkJycDCsrKzg7O2vs6+7ujuTk5AK/LQ4bERERyZQ+h40SEhLg5OQkrbe2tn7lcYMGDcKlS5dw9OhR3QJ4CSYvRERE9FpOTk4aycurDB48GD/99BMOHz6MkiVLSus9PDyQk5OD1NRUjepLSkoKPDw8ChwLh42IiIjkqohnG4miiMGDB2P79u3Yv38/fH19NbbXqlULlpaW2Ldvn7QuNjYW8fHxCAgIKPB1WHkhIiKSqaKebTRo0CBs2LABP/74IxwdHaU+FoVCAVtbWygUCoSEhCAsLAyurq5wcnLCkCFDEBAQUOCZRgCTFyIiItKTpUuXAgCaNGmisT4qKgo9e/YEAERGRsLMzAwdO3aEUqlEUFAQlixZotV1mLwQERHJ1XOzhXQ6R4F3ff2+NjY2WLx4MRYvXvzGITF5ISIikik+VZqIiIhMi0yfKs3ZRkRERGRSWHkhIiKSKQ4bERERkWlRi/mLrucwMhw2IiIiIpPCygsREZFcybRhl8kLERGRTAnQQ8+LXiLRLw4bERERkUlh5YWIiEiuivgOu0WFyQsREZFMyXWqNIeNiIiIyKSw8kJERCRXnG1EREREpkQQRQg69qzoenxhYPJCREQkV+q/F13PYWTY80JEREQmhZUXIiIimeKwEREREZkWmTbsctiIiIiITAorL0RERHLFO+wSERGRKZHrHXaZvJBRa9U1Ca26JsH9HSUA4M51O2xY4o0zh10NHBnpGz9r+fo8MAaNq91G6RKpUOaa4+ItdyzdVQ/x952lfdoEXEHzWnHw8/4L9ja5CBrTA5lPrQ0XNBk19rwYmI+PD+bPn2/oMIzWX8lWiJrjgyEdqmNox+q4cFKBCYuvoFS5LEOHRnrGz1q+qpdLwrYjldAvsi2GL2kFC3M1Igf8AhurXGkfG6s8nLrqjXXRNQwYqQw9GzbSdTEyrLyQUTt1wE3j9dr5PmjVNRn+1TMQH2dvoKioMPCzlq8Ry1pqvJ7+XRP8PGM9/Lz/woUbngCAzYeqAABqlEss8vjkTFDnL7qew9iw8vIaOTk5hg6B/mZmJqJxywewsVPhaoyTocOhQsTPWt7sbfN/rqY/4bAQvRnZJS9NmjTB0KFDMWrUKLi6usLDwwOTJk2StsfHx6Nt27ZwcHCAk5MTunTpgpSUFGn7pEmTUL16daxcuRK+vr6wsbEBAAiCgOXLl6N169aws7NDxYoVceLECcTFxaFJkyawt7dHgwYNcOPGDelcN27cQNu2beHu7g4HBwfUqVMHe/fu1fo9KZVKpKenayxvE58KWdh27jh2XjyGwZPjMHVQRcTfsDN0WFQI+FnLnyCIGNbhBC7cdMetJPYzFTqZDhvJLnkBgLVr18Le3h6nTp3CrFmzMGXKFERHR0OtVqNt27Z49OgRDh06hOjoaNy8eRMff/yxxvFxcXH44YcfsG3bNpw/f15aP3XqVHTv3h3nz5+Hv78/Pv30U3zxxRcYO3Yszpw5A1EUMXjwYGn/zMxMtGzZEvv27UNMTAxatGiB4OBgxMfHa/V+IiIioFAopMXb21unvx9Tc/eWLQa1q4HhXarj5+89MeKrayhV9omhw6JCwM9a/kZ0OooyHo8wcU0zQ4fydhD1tBgZWfa8VK1aFRMnTgQAlC9fHl9//TX27dsHALh48SJu3bolJQDr1q1D5cqVcfr0adSpUwdA/lDRunXrULx4cY3z9urVC126dAEAjB49GgEBARg/fjyCgoIAAMOGDUOvXr2k/atVq4Zq1apJr6dOnYrt27dj586dGknO64wdOxZhYWHS6/T09LcqgcnLNUNSvC0AIO6yAypUyUDb7olYNLGcgSMjfeNnLW9hHY+iQeV4DFoYjAdpDoYO560g18cDyLLyUrVqVY3Xnp6euH//Pq5cuQJvb2+NL/5KlSrB2dkZV65ckdaVLl36hcTl3+d1d3cHAFSpUkVjXXZ2tjSsk5mZifDwcFSsWBHOzs5wcHDAlStXtK68WFtbw8nJSWN5mwlmgKWVEXaQkd7xs5YLEWEdj6JR1dsYurg1kh693T/DSHeyrLxYWlpqvBYEAWp1wX8A2tu/fGbD8+cVBOE/1z27Vnh4OKKjozFnzhyUK1cOtra26NSpE5uAtdAz7DbOHHbB/SRr2Nmr0KT1A1Stm4ZxIZUNHRrpGT9r+RrR+Ria14zDmJUf4km2JVwd84cCM7OtkJOb/zXk6vgEbk5PULJY/i9/ZT0f4YnSEsmPHZDxxMZgsZs83mHX9FWsWBEJCQlISEiQqi9//vknUlNTUalSJb1f79ixY+jZsyfat28PIL8Sc/v2bb1fR86c3XIR/tU1uJbIQVaGBW7F2mFcSGXEHHcxdGikZ/ys5avD+38CABYP/Ulj/fTvGuOX3/0AAO3e+xMhH52Tti0ZtuuFfegNiAB0LV4aX+7ydiUvgYGBqFKlCrp164b58+cjLy8PAwcOROPGjVG7dm29X698+fLYtm0bgoODIQgCxo8fr1UFiID5X5Y3dAhURPhZy9d7w/q9dp/Vu2tj9W79/xwmeZJlz8t/EQQBP/74I1xcXNCoUSMEBgaiTJky2LRpU6Fcb968eXBxcUGDBg0QHByMoKAg1KxZs1CuRURE9G/PGnZ1XYyNIIpGGBW9Unp6OhQKBT5w7AYLwcrQ4RCRHqW30P8QNhmXvNxsnNk+HmlpaYU2AUP6nqg+Bhbmut0MME+lxP7zMws1Xm29VZUXIiIiMn1vVc8LERHRW4WzjYiIiMikqAEIejiHkeGwEREREenN4cOHERwcDC8vLwiCgB07dmhsF0UREyZMgKenJ2xtbREYGIjr169rdQ0mL0RERDJliNlGWVlZqFatGhYvXvzS7bNmzcLChQuxbNkynDp1Cvb29ggKCkJ2dnaBr8FhIyIiIrkyQM/LRx99hI8++ug/TiVi/vz5GDduHNq2bQsg/xmD7u7u2LFjBz755JMCXYOVFyIiIrl6lrzouiB/+vXzi1Kp1DqcW7duITk5GYGBgdI6hUKBevXq4cSJEwU+D5MXIiIiei1vb28oFAppiYiI0PocycnJAP55uPEz7u7u0raC4LARERGRXOlx2CghIUHjJnXW1rrd/E4XrLwQERHJlVpPCwAnJyeN5U2SFw8PDwBASkqKxvqUlBRpW0EweSEiIqIi4evrCw8PD+zbt09al56ejlOnTiEgIKDA5+GwERERkUzp48GK2h6fmZmJuLg46fWtW7dw/vx5uLq6olSpUhg+fDimTZuG8uXLw9fXF+PHj4eXlxfatWtX4GsweSEiIpIrA0yVPnPmDJo2bSq9DgsLAwD06NEDa9aswahRo5CVlYV+/fohNTUV77//Pnbv3g0bG5sCX4PJCxEREelNkyZNIL4i4REEAVOmTMGUKVPe+BpMXoiIiORKLQKCjpUXNR/MSEREREVFpk+V5mwjIiIiMimsvBAREcmWHiovML7KC5MXIiIiuZLpsBGTFyIiIrlSi9C5cmKEDbvseSEiIiKTwsoLERGRXInq/EXXcxgZJi9ERERyJdOeFw4bERERkUlh5YWIiEiuZNqwy+SFiIhIrjhsRERERGR4rLwQERHJlQg9VF70EoleMXkhIiKSKw4bERERERkeKy9ERERypVYD0PEmc2repI6IiIiKikyHjZi8EBERyZVMkxf2vBAREZFJYeWFiIhIrniHXSIiIjIloqiGqONToXU9vjBw2IiIiIhMCisvREREciWKug/7GGHDLpMXIiIiuRL10PNihMkLh42IiIjIpLDyQkREJFdqNSDo2HBrhA27TF6IiIjkisNGRERERIbHygsREZFMiWo1RB2HjYzxPi9MXoiIiORKpsNGTF6IiIjkSi0CgvySF/a8EBERkUlh5YWIiEiuRBGArlOlja/ywuSFiIhIpkS1CFHHYSPRCJMXDhsRERGRSWHyQkREJFeiWj+LlhYvXgwfHx/Y2NigXr16+P333/X6tpi8EBERyZSoFvWyaGPTpk0ICwvDxIkTce7cOVSrVg1BQUG4f/++3t4XkxciIiLSm3nz5qFv377o1asXKlWqhGXLlsHOzg6rV6/W2zXYsGuCnjVP5Ym5Bo6EiPQtLzfb0CFQIVP9/RkXRSNsnqjU+cGKecj/rklPT9dYb21tDWtra411OTk5OHv2LMaOHSutMzMzQ2BgIE6cOKFTHM9j8mKCMjIyAACHMzcbOBIi0rvthg6AikpGRgYUCkWhnNvKygoeHh44mvyLXs7n4OAAb29vjXUTJ07EpEmTNNb99ddfUKlUcHd311jv7u6Oq1ev6iUWgMmLSfLy8kJCQgIcHR0hCIKhwykS6enp8Pb2RkJCApycnAwdDhUiftZvl7fx8xZFERkZGfDy8iq0a9jY2ODWrVvIycnRy/lEUXzh++bfVZeixOTFBJmZmaFkyZKGDsMgnJyc3pofcG87ftZvl7ft8y6sisvzbGxsYGNjU+jXeV6xYsVgbm6OlJQUjfUpKSnw8PDQ23XYsEtERER6YWVlhVq1amHfvn3SOrVajX379iEgIEBv12HlhYiIiPQmLCwMPXr0QO3atVG3bl3Mnz8fWVlZ6NWrl96uweSFTIK1tTUmTpxo0DFWKhr8rN8u/Lzl5+OPP8aDBw8wYcIEJCcno3r16ti9e/cLTby6EERjfGgBERER0X9gzwsRERGZFCYvREREZFKYvBAREZFJYfJCREREJoXJCxEREZkUJi9ERERkUpi8EJGs/ftuELw7hGn65ZdfcOHCBUOHQUaCyQvJDr+s6JmkpCTpYXLfffcdALw1DzOVC1EUERcXh86dO2P+/Pn4888/DR0SGQEmLyQrarVa+nLKzMwEwC+rt1V0dDQ6deqE06dPIzQ0FJ9//jnu3Llj6LBIS4IgoFy5cvj+++9x6NAhzJs3D5cvXzZ0WGRgfDwAyYYoijAzy8/HZ86ciWPHjuHp06cYNWoU6tWrVyRPcSXj8e677+LJkyfo3Lkz0tLSEBMTg9KlS0OtVkv/Tsj4iaIIQRDQpk0bmJmZYeDAgQCA0NBQVK5c2cDRkaHwfzDJwvMVl/nz52PmzJmoXbs2njx5gkGDBuGbb77Bw4cPDRwlFZW8vDx4enoiODgYSUlJKFeuHDIzM6XEhUOJpkMQBOnzat26NRYvXow9e/YgMjKSFZi3GJMXkoVnv0n/+eefuHLlCrZu3YqJEyfi+PHjCA4Oxrp167Bq1SopgeGXlzw9+1wtLPKLyu+99x6io6NhZmaGcePGYd++fdJv8i87jozLs8/l+c8rODgYixYtYgLzlmPyQrKxdetWNG3aFHv27IGNjY20ft68eWjevDnWr1+P1atX48GDB+yDkaHnq283b95ERkYGGjZsiEaNGmHLli3IyMjAjBkzcODAAemYefPmAWBflDF6lmT+/vvvWLt2LebPn4/bt28jLy8Pbdu21Uhg2MT7FhKJTJRKpXphXffu3UUrKytxxowZYmZmpsa28PBwsXjx4uK3335bVCGSAYwbN06sWLGiWLZsWXH48OHiH3/8IYqiKMbHx4t16tQRGzVqJM6cOVNs3bq1qFAoxLy8PANHTP+mVqtFURTFH374QXRxcRGbN28uvvPOO2KTJk3Eb775RlQqlaIoiuKOHTvEMmXKiB9//LF45coVQ4ZMRYwNu2Syng0V/fTTT3B0dETjxo2xdu1a5OTkYN26dShZsiQ6duwIOzs7AMDs2bNRqlQpfPLJJ4YMm/RMfG4Y6IcffsDKlSuxePFinDx5EufOncOIESPw1VdfoUaNGti2bRuGDBmC3377DdbW1njw4AHMzc3ZxGtkBEHA4cOHMXDgQMyePRshISG4du0aKleujIyMDCiVSvTr1w9t27aFUqnE1KlT2ZD/lhFEkYO9ZFqe/6L5/fff8dlnn6FWrVoYOXIkatasCQDo0qULLl26hLFjx2okMM+oVCqYm5sXeexUeH755Rfs378f/v7+6NOnDwBgx44dWL58OXJzczFr1izUrFkTWVlZUCqVcHFxgSAIyMvLk3pkyDioVCrMnz8fCQkJmD9/Pm7evInmzZvjvffeQ1paGs6fP4+xY8eiV69esLa2RmZmJhwcHAwdNhUhJi9kUp7/LXvq1Kl48OABdu7cicTERLRv3x5hYWGoV68eAODjjz/GlStXMHDgQOmHHMnTuXPn0KdPH9y5cwdfffWVlLwA/yQwKpUK06dPR506daRtrLgYr2vXrkGlUqFUqVJo0aIFKlSogFWrViEpKQmVK1eGu7s7hg0bhv79+7+0CZvkjf9ryWQ8/wNq7ty5mD17Ntq3b49ffvkFixcvxoULF7Bo0SKcPn0aALBp0yaUKFECR48ehZWVlSFDp0JWs2ZNDBgwAJ6enoiKisLt27elbe3atcOAAQPw+PFjfPvttxrHMXExDi/7HdrX1xcVK1bExYsX8fjxYwwbNgwAkJKSgjp16qB+/fpo2bIlADZcv41YKyWjt2XLFnTu3Fn6AaVSqXDw4EF0794dTZs2BQBUqlQJ9vb2CAsLQ25uLsLDw1GnTh3s3bsXKpVKulcEf8iZvv+qlvTt2xcAsHbtWowbNw7Tp09H6dKlAQBt2rSBi4sL3nvvvSKNlV7v2f/L6Oho/Pjjj7C3t0fnzp1Ru3ZtAEBWVhaePn2KuLg4VKxYETt27ICnpycWLVrEoaK3GIeNyKjNmjULFy9exNq1a6UvLJVKhU8++QQODg6IiorS6F8ZM2YMlixZgk6dOmHo0KGoXr06AA4PyMXzn+PWrVtx6dIlFC9eHFWrVkXDhg0BAEuXLsWGDRtQunRpzJgxA6VKlfrPc5Bx2LNnDzp06ID3338fDx8+xOXLl7Fp0yYEBwfjwYMH6NKlC+7evQsLCwvcv38fe/fuRY0aNQwdNhkQkxcyaklJSShevDgsLCxw+vRpqV8hIiIC06dPx9GjR6UEBci/b8fPP/+Mv/76C+3bt8ekSZNYcZGJ5z/H0aNH49tvv0W1atXw9OlTPHnyBMOGDcOnn34KID+B2bRpE+zs7BAVFQV3d3dDhk6vsWjRIpibm2PgwIFITEzE7NmzsWjRImzcuBGdOnVCUlISfv31V2RnZ+PDDz9EuXLlDB0yGVqRT84megM///yz6OfnJ86ZM0daFxwcLHp6eopHjx4VU1JSxOzsbLFt27bi1q1bxZkzZ4p2dnbi/fv3DRg1FYavv/5a9PHxEY8fPy69trKyEsuUKSOuWLFC2m/27NnigAEDXno/IDKsZ/dxuXr1qhgTEyN27dpV4/5LqampYmhoqGhubi5u3brVUGGSEWPPC5kEPz8/vP/++9i+fTvMzc0xfPhwbNiwAb169cJHH30ELy8vqNVqAPkNmvv370fJkiU5HVpmnj59iosXL2LIkCEICAjAzp078eWXX2L06NG4du0aJk+eDDs7O3z66acIDw+XqjUcKjIugiBg+/bt+Pzzz1GmTBlcvnwZ5cqVkz4nhUKBiRMnwtzcHJ07d8bOnTvRunVrQ4dNRoTDRmR0/v1F8+z1jRs3MGvWLFy4cAGffvophg4dCiD/xmSpqalQqVQICQmBubk5Bg8ejHPnzmH37t1wcnIy1FshHYkvGfJLSkrC06dPkZeXh5YtW2Lw4MEYPnw4tm7dim7dusHKygrr1q1D+/bt//McZBjPPouEhAS0b98e/fr1g5+fH3bv3o3Zs2dj5cqV6Nmzp7R/amoqZs+ejc8++wwVK1Y0XOBkdFh5IaMiiqKUuCxfvhzXrl2Dg4MD+vTpg7Jly2LkyJGYPXs2NmzYAJVKhdDQUHTs2FE6/tq1a5g7dy62bt2KAwcOMHExYc8nsc/fSM7T0xMAsG7dOhQrVgy9e/cGADg4OKB169Zo0aIF2rRpI52HiYvxEAQBe/bswbFjx1C1alX06tULlpaWaNy4MaysrNCnTx+IoohevXoBAJydnTFt2jR+hvQCJi9kNJ7/shozZgxWr16NatWq4f79+4iKikJ0dDT8/PwwatQozJ49G1u3bkVWVhbGjRsHAEhPT8eff/6J+Ph4HDhwAFWrVjXk2yEdPJ/Ezps3D3/88QeUSiUmTpyIMmXKwMrKChYWFrh9+zaOHTuGJk2aYPHixdLddQVB4F2UjciziktGRgbu37+PqVOnomTJkkhMTJSms0+ePBmCIGDQoEHIzs7GgAEDADD5pP9gqGYbov+SkpIiDhkyRDx37pwoiqJ46dIlsWXLlqKzs7N49epVURRFMS4uTuzSpYvYt29fqflPFEUxOztbzMjIMEjcpB/PN9hOnz5ddHJyEgcMGCCWL19eLFmypLhlyxZRqVSKcXFxYocOHUQXFxexTJky4rvvvivm5OSIoihq/Jsg4/Ddd9+JFhYWolqtFpcvXy4KgiBOmzZNTE1N1dhv5MiRYrFixV5YT/Q8Ji9kVNavXy86ODiIdevWFePj46X1169fF1u2bCm6uLhICczdu3elLzrOKJGfO3fuiL169RKPHj0qrevUqZNYqlQpccuWLaIoiuKNGzfEXbt2iatXr5aeDp2bm2uQeOlFz5LIBw8eiCEhIeK8efOkbbNnzxYFQRBnz54tpqWlaRz34MGDIo2TTA+TFzIq+/fvF4OCgkQHBwfxzp07oij+8wMwLi5ODA4OFgVBkLaJIhMXOVq1apVoa2srVqlSRfzjjz80tnXu3Fn09vYWt27dKj59+lRj27MEhozH6dOnxYYNG4oNGzYUY2NjpeqYKP6TwMybN4+VFtIK5w6SwTyb2vy8Jk2aYOrUqfDz80NgYCAePHgg3dq/bNmymD17NkaMGAEvLy/pGE6BNX3//rfQu3dvNGjQAJcuXcLVq1ehUqmkbZs3b0aDBg3w6aef4vjx4xrHscfF+Fy5cgVPnjzBhQsXYGdnB0tLSyiVSgBAeHg45syZgxEjRmD9+vUvfcYR0ctwqjQZxPPNudu3b0diYiLUajWaN28Of39/nDt3DkOHDsXjx49x4MABlChR4oUpr8/PQCF5OHjwIEqUKIFKlSoBAN5//33cu3cP69evR4MGDTQS1fHjx2PSpElMWIxcXl4etm3bhi+//BIeHh7YsWMH3NzckJOTIz0wddGiRWjWrJn0uRO9DpMXMqhRo0bh22+/xXvvvYe4uDgIgoDBgwejd+/eOH78OMaMGYPU1FTs2bMHHh4ehg6X9Oz5JPbkyZNo3rw5Bg0ahL59+6Js2bIAgICAANy/fx9r1659IYEBwFlFRkR87j4uoiji6dOn8PPzgyiK2Lp1K+bOnYtixYph/fr1cHFxgVKphLW1taHDJhPEejsZzPfff4/vv/8eO3fuxJYtWzB06FBcvnwZzs7OAIAGDRpgzpw5yMnJQXh4uGGDJb0Tn5sO/dVXX2Hv3r2wsLDAokWLsHTpUsTFxQEATpw4AXd3d/Tu3RsHDhx4YWiBiYtxeJa4bNu2DYGBgWjatCnq1auHgQMHIiEhAZ07d0ZoaCgePXqEnj174uHDh0xc6M0ZpNOGSBTFKVOmiJ9++qkoiqK4efNm0cnJSVy6dKkoiqKYkZEh3rhxQxRFUbx48SIbMWXm+anMM2bMEBUKhbh7927xwIED4tSpU0VHR0dx5MiRYlxcnLRf2bJlxc6dOxsiXCqggwcPira2tuLSpUvFAwcOiNu2bROLFSsmtm/fXpoduGHDBrFSpUpily5d2GxPb4wNA1QkXvZsmaysLPj4+ODkyZPo3bs3Zs+ejf79+0MURWzZsgWPHj3C0KFD8e677wLg8IAcHDx4EE2aNJF6l7Kzs7F7924MHToUQUFBAPKbthUKhfRson79+qF8+fKIi4tDXl6eIcOn19izZw+aNm2K/v37S+t8fX3RrFkzzJkzB5GRkejcuTMsLS1Ru3ZtNtvTG+O/HCp0KpVK+iF148YNJCYmIjc3F23atEFERAQaNGiA1atXSz/wnjx5go0bNyIxMRGWlpbSeZi4mLbhw4dj48aNGsM+KpUKubm50rpns1CGDBmCrl27YvXq1Vi3bh3u3r0LALCwsNCYeUTGQxRFJCUlSQmmWq1GTk4OqlevjgULFmDDhg24c+cOLCws0KlTJ/j4+Bg2YDJpTF6o0CxduhQxMTFS0jF69Gi0bt0aVatWRWBgIP744w+sXLkSVlZWyM3NxZ07d3Dx4kV07NgRDx48wFdffWXgd0D6NHDgQCxatAiCIODq1asAAHt7e9SpUwfLly+XeiCeffl5enrC398fS5cuxZ49ewDkf0EyiTUOzxLOR48e4cmTJxAEAcHBwTh06BD27t0LMzMzaTagg4MD3Nzc4OjoaMiQSUaYvFChuHXrFmbMmIGlS5fixo0b2L59O9atW4eZM2di7ty5qFevHoYPH47Tp09j1qxZCAkJQUBAALp3746cnBycOnWKv2XLxJIlS5CUlIQKFSrA0tIS3333Hbp3745NmzYByH+mTbly5dCgQQPcvXsXKpUKarUasbGxmDVrFnr06IEvv/xS+oIk4yAIAnbs2IE2bdqgevXqmDhxImxtbdG/f38MGTIE0dHRUsX11KlTsLOz4+dHesOeFyoUvr6+2LVrF/r06YNFixZBqVRi1KhRaNu2LYD8hyj6+PhgzJgx+P7773H58mUkJCTAyckJ1apVg5mZGe/jIgO7du3C/Pnzcf78ecyYMQPFihVDpUqV4ODggDVr1sDKygrt27fHihUrMGjQIFSuXBmVK1fG48ePkZeXh/r16yMuLg579uxhxcXInDt3Dj179sSIESPw8OFD/Pzzz7h27Rrq1q2Ljz76CK1atULNmjVhaWmJS5cuYf/+/XBxcTF02CQTvM8LFapz587hiy++wI0bNxAWFiY9ARoAHj58iJCQEHh7e2PRokUax72swZdMU2RkJH744Qf4+/tj+vTpcHd3x6VLlxAWFgZRFDFkyBC0adMGQP5Q46NHj2BpaYmwsDBYWFigf//+uHnzJrZv3w57e3sDvxsC8nvXvv/+ewiCgC+//BJAfqK6cOFCuLi44LPPPoNCocCvv/4KV1dXtG/fHuXLlzdw1CQnTF6o0F28eBFt2rSBq6srVq5ciRo1akjb+vTpg8TERPzyyy8GjJAKw/MJaGRkJLZu3Qp/f3/MmDED7u7uuHjxIkaMGAFRFNG/f3907NhR4/h79+4hIiIC33//PQ4dOiTNOiPDSk9PR7NmzRAfH4/evXsjIiJC2rZr1y5ERkbCxcUF48ePR/Xq1Q0XKMkaf7WlQlelShX8+OOPUKlU0hACAGRkZODKlSsoWbKkYQOkQmFmZib1LIWGhqJjx464evUq/ve//yE5ORlVqlTBvHnzYGZmhhUrVmDdunXSsQ8ePMCvv/4qDTcwcTEeTk5O+Oabb+Ds7IwjR47g8uXL0rbg4GCEh4fj5s2bmDNnDp48ecLnFVGhYOWFikxMTAw+++wzPH78GLVr14a1tTVu3LiBU6dOwdLS8oVnF5FpetWQ39y5c7Ft2zZpCMnDwwOXLl1Cjx490KhRI0RGRkr7Pnr0CObm5lAoFEUVOmnhjz/+QI8ePVC3bl0MHToUlStXlrbt2bMHfn5+KF26tAEjJDlj8kJF6tKlS2jfvj1sbGwwcuRIdOvWDebm5mzOlYnnE5cff/wRV69ehYeHBypXrozatWsD+CeBqVixotQDc/PmTfj4+MDMzIz9TiYkJiYGffr0Qc2aNREaGsoHK1KRYfJCRe706dNYuXIlli1bBkEQ+GUlE89XzkaPHo0NGzagbNmyUKvVUKlUGDNmDIKDgwHkJzA//vgjihUrhpUrV8LV1RUAG7VNUUxMDPr3748yZcpg4sSJ8Pf3N3RI9BbgTwkqcnXq1GHiIkPPEpdFixZh06ZN2Lx5Mw4ePIj27dvjzJkzCA8Px5YtWwAAI0aMQLNmzVCiRAnpQZwA+G/BBNWoUQNff/01kpKSOMRHRYaVFzIY9rjIT0ZGBgYPHow6depg8ODB2LVrFz7//HMMHDgQly9fxqVLl7BgwQK0bt0awD//BpjEmr7s7GzY2NgYOgx6SzB5IaI39rKk4/r16zA3N0d2djZat26N4cOHY+jQoVizZg369u0LBwcHbNq0CR9++CEAJrFEpD12SBLRG3k+cdm9ezfS0tJQpUoVqWlzxYoVKFmyJEJCQgAALi4uCA4ORrNmzdCsWTPpPExciEhbrNMS0Rt5lriMHTsWnTp1wvjx41GtWjV8/fXXyM3NhaWlJeLi4nDu3Dnk5ORg1apV8Pf3x8CBA2Fubs7nVhHRG2PlhYi08myYRxRF3LlzB0ePHkV0dDT8/PwQFRWFoUOHIisrC3Xr1kX9+vXRrl07uLq6wsrKCtu2bZOO5bOKiOhNseeFiArs+aGiR48e4eHDh1i9ejWmTZsmJSMLFixAWFgY5s+fjypVqiA1NRXJycno06eP9KRwJi5EpAsmL0SktS+//BLR0dG4du0aSpcujc2bN8PPz0/aPn/+fIwePRojR47EtGnTpPVMXIhIH9jzQkSvpVarpT9v3LgRUVFR+Pzzz9GrVy/ExcVh5cqVuHPnjrTP8OHDMWHCBBw4cEDj2TZMXIhIH1h5IaICO3ToEDZv3ox69eqhe/fuAIAlS5YgIiIC3bp1w4ABAzSeZ/N8fwxnFRGRvrBhl4gKJDk5GSEhIUhJSUGFChWk9QMHDoQoipg5cybMzc0REhKCMmXKAAATFyIqFBw2IqIC8fDwwLZt2+Dl5YWff/4ZFy9elLYNGjQI//vf//DVV19hz549GscxcSEifeOwERFp5cKFC+jVqxdq166NYcOGoXLlytK2bdu2oW3btuxtIaJCxeSFiLQWExODPn36oFatWhg+fLh0V91nOKuIiAoTkxcieiMxMTH44osvULp0acyaNQu+vr6GDomI3hLseSGiN1KjRg18/fXXcHR01JhhRERU2Fh5ISKdPJtN9LInTBMRFQYmL0SkM06HJqKixF+TiEhnTFyIqCgxeSEiIiKTwuSFiIiITAqTFyIiIjIpTF6IiIjIpDB5ISIiIpPC5IWI3kjPnj3Rrl076XWTJk0wfPjwIo/j4MGDEAQBqamp/7mPIAjYsWNHgc85adIkVK9eXae4bt++DUEQcP78eZ3OQ0QvYvJCJCM9e/aEIAgQBAFWVlYoV64cpkyZgry8vEK/9rZt2zB16tQC7VuQhIOI6L9YGDoAItKvFi1aICoqCkqlEr/88gsGDRoES0tLjB079oV9c3JyYGVlpZfrurq66uU8RESvw8oLkcxYW1vDw8MDpUuXxoABAxAYGIidO3cC+GeoZ/r06fDy8oKfnx8AICEhAV26dIGzszNcXV3Rtm1b3L59WzqnSqVCWFgYnJ2d4ebmhlGjRuHfN+f+97CRUqnE6NGj4e3tDWtra5QrVw6rVq3C7du30bRpUwCAi4sLBEFAz549AQBqtRoRERHw9fWFra0tqlWrhq1bt2pc55dffkGFChVga2uLpk2basRZUKNHj0aFChVgZ2eHMmXKYPz48cjNzX1hv+XLl8Pb2xt2dnbo0qUL0tLSNLavXLkSFStWhI2NDfz9/bFkyRKtYyEi7TF5IZI5W1tb5OTkSK/37duH2NhYREdH46effkJubi6CgoLg6OiII0eO4NixY3BwcECLFi2k4+bOnYs1a9Zg9erVOHr0KB49eoTt27e/8rrdu3fH999/j4ULF+LKlStYvnw5HBwc4O3tjR9++AEAEBsbi6SkJCxYsAAAEBERgXXr1mHZsmW4fPkyQkND8dlnn+HQoUMA8pOsDh06IDg4GOfPn0efPn0wZswYrf9OHB0dsWbNGvz5559YsGABVqxYgcjISI194uLisHnzZuzatQu7d+9GTEwMBg4cKG3/7rvvMGHCBEyfPh1XrlzBjBkzMH78eKxdu1breIhISyIRyUaPHj3Etm3biqIoimq1WoyOjhatra3F8PBwabu7u7uoVCqlY9avXy/6+fmJarVaWqdUKkVbW1vxt99+E0VRFD09PcVZs2ZJ23Nzc8WSJUtK1xJFUWzcuLE4bNgwURRFMTY2VgQgRkdHvzTOAwcOiADEx48fS+uys7NFOzs78fjx4xr7hoSEiF27dhVFURTHjh0rVqpUSWP76NGjXzjXvwEQt2/f/p/bZ8+eLdaqVUt6PXHiRNHc3Fy8e/eutO7XX38VzczMxKSkJFEURbFs2bLihg0bNM4zdepUMSAgQBRFUbx165YIQIyJifnP6xLRm2HPC5HM/PTTT3BwcEBubi7UajU+/fRTTJo0SdpepUoVjT6XCxcuIC4uDo6Ojhrnyc7Oxo0bN5CWloakpCTUq1dP2mZhYYHatWu/MHT0zPnz52Fubo7GjRsXOO64uDg8efIEzZs311ifk5ODGjVqAACuXLmiEQcABAQEFPgaz2zatAkLFy7EjRs3kJmZiby8PDg5OWnsU6pUKbzzzjsa11Gr1YiNjYWjoyNu3LiBkJAQ9O3bV9onLy8PCoVC63iISDtMXohkpmnTpli6dCmsrKzg5eUFCwvN/+b29vYarzMzM1GrVi189913L5yrePHibxSDra2t1sdkZmYCAH7++WeNpAHI7+PRlxMnTqBbt26YPHkygoKCoFAosHHjRsydO1frWFesWPFCMmVubq63WIno5Zi8EMmMvb09ypUrV+D9a9asiU2bNqFEiRIvVB+e8fT0xKlTp9CoUSMA+RWGs2fPombNmi/dv0qVKlCr1Th06BACAwNf2P6s8qNSqaR1lSpVgrW1NeLj4/+zYlOxYkWp+fiZkydPvv5NPuf48eMoXbo0vvzyS2ndnTt3XtgvPj4eiYmJ8PLykq5jZmYGPz8/uLu7w8vLCzdv3kS3bt20uj4R6Y4Nu0RvuW7duqFYsWJo27Ytjhw5glu3buHgwYMYOnQo7t69CwAYNmwYZs6ciR07duDq1asYOHDgK+/R4uPjgx49eqB3797YsWOHdM7NmzcDAEqXLg1BEPDTTz/hwYMHyMzMhKOjI8LDwxEaGoq1a9fixo0bOHfuHBYtWiQ1wfbv3x/Xr1/HyJEjERsbiw0bNmDNmjVavd/y5csjPj4eGzduxI0bN7Bw4cKXNh/b2NigR48euHDhAo4cOYKhQ4eiS5cu8PDwAABMnjwZERERWLhwIa5du4aLFy8iKioK8+bN0yoeItIekxeit5ydnR0OHz6MUqVKoUOHDqhYsSJCQkKQnZ0tVWJGjBiBzz//HD169EBAQAAcHR3Rvn37V5536dKl6NSpEwYOHAh/f3/07dsXWVlZAIB33nkHkydPxpgxY+Du7o7BgwcDAKZOnYrx48cjIiICFStWRIsWLfDzzz/D19cXQH4fyg8//IAdO3agWrVqWLZsGWbMmKHV+23Tpg1CQ0MxePBgVK9eHcePH8f48eNf2K9cuXLo0KEDWrZsiQ8//BBVq1bVmArdp08frFy5ElFRUahSpQoaN26MNWvWSLESUeERxP/quCMiIiIyQqy8EBERkUlh8kJEREQmhckLERERmRQmL0RERGRSmLwQERGRSWHyQkRERCaFyQsRERGZFCYvREREZFKYvBAREZFJYfJCREREJoXJCxEREZmU/wN1bl2A35oYQwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "y_true = outputs.label_ids\n",
    "y_pred = outputs.predictions.argmax(1)\n",
    "\n",
    "labels = train_ds.features['label'].names\n",
    "cm = confusion_matrix(y_true, y_pred)\n",
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=labels)\n",
    "disp.plot(xticks_rotation=45)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9178d2d1-b828-45a6-8873-039abc0419c2",
   "metadata": {},
   "source": [
    "### What is Recall Score?\n",
    "The recall score is a performance metric used in classification tasks to measure the ability of a model to correctly identify all relevant instances within a dataset. Specifically, recall assesses the proportion of actual positives that are correctly predicted as such by the model.\n",
    "\n",
    "Let's print recall scores using scikit-learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "48d87ca7-8458-41d5-a773-38e2c9522f64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall for benign: 0.90\n",
      "Recall for malignant: 0.86\n",
      "Recall for normal: 0.78\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import recall_score\n",
    "\n",
    "# Calculate the recall scores\n",
    "# 'None' calculates recall for each class separately\n",
    "recall = recall_score(y_true, y_pred, average=None)\n",
    "\n",
    "# Print the recall for each class\n",
    "for label, score in zip(labels, recall):\n",
    "    print(f'Recall for {label}: {score:.2f}')\n"
   ]
  },
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    "`Recall for benign: 0.90,\n",
    "Recall for malignant: 0.86,\n",
    "Recall for normal: 0.78`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67b76567-039d-467b-9cfc-0837fb3e1a1b",
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   "source": [
    "## Conclusion\n",
    "In this cookbook, we covered how to train a ViT model with a medical dataset. It covers crucial steps such as dataset preparation, image preprocessing, model configuration, training, evaluation, and result visualization. By leveraging Hugging Face's Transformers library scikit-learn and PyTorch Torchvision, it facilitates efficient model training and evaluation, providing valuable insights into the model's performance and its ability to classify biomedical images accurately."
   ]
  }
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